{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "7.4. 动量法.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chongzicbo/Dive-into-Deep-Learning-tf.keras/blob/master/7.4.%20%E5%8A%A8%E9%87%8F%E6%B3%95.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OfTvPUb8finD",
        "colab_type": "text"
      },
      "source": [
        "##7.4. 动量法\n",
        "在“梯度下降和随机梯度下降”一节中我们提到，目标函数有关自变量的梯度代表了目标函数在自变量当前位置下降最快的方向。因此，梯度下降也叫作最陡下降（steepest descent）。在每次迭代中，梯度下降根据自变量当前位置，沿着当前位置的梯度更新自变量。然而，如果自变量的迭代方向仅仅取决于自变量当前位置，这可能会带来一些问题。\n",
        "\n",
        "###7.4.1. 梯度下降的问题\n",
        "让我们考虑一个输入和输出分别为二维向量$\\boldsymbol{x} = [x_1, x_2]^\\top$ 和标量的目标函数$f(\\boldsymbol{x})=0.1x_1^2+2x_2^2$。与“梯度下降和随机梯度下降”一节中不同，这里将$x_1^2$系数从 1 减小到了 0.1 。下面实现基于这个目标函数的梯度下降，并演示使用学习率为 0.4 时自变量的迭代轨迹。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_OULOd4BNzN",
        "colab_type": "code",
        "outputId": "97c7e01b-5bc2-4094-96c6-95b4991f3981",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        }
      },
      "source": [
        "%matplotlib inline\n",
        "import math\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from IPython import display\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import losses\n",
        "from tensorflow.data import Dataset\n",
        "import time"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZw1gd8VBTP9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.enable_eager_execution()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KgDunhF3BWS1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "eta=0.4\n",
        "def f_2d(x1,x2):\n",
        "  return 0.1*x1**2+2*x2**2\n",
        "\n",
        "def gd_2d(x1,x2,s1,s2):\n",
        "  return (x1-eta*0.2*x1,x2-eta*4*x2,0,0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6VgDnjlB1A8",
        "colab_type": "code",
        "outputId": "f44440e4-bb89-4fa6-b5e4-dfa72df12c3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "def show_trace_2d(f,results):\n",
        "  plt.plot(*zip(*results),'-o',color='#ff7f0e')\n",
        "  x1,x2=np.meshgrid(np.arange(-5.5,1.0,0.1),np.arange(-3.0,1.0,0.1))\n",
        "  plt.contour(x1,x2,f(x1,x2),colors='#1f77b4')\n",
        "  plt.xlabel('x1')\n",
        "  plt.ylabel('x2')\n",
        "def train_2d(trainer):\n",
        "  x1,x2,s1,s2=-5,-2,0,0 #s1,s2是自变量状态,本章后续几节会使用\n",
        "  results=[(x1,x2)]\n",
        "  for i in range(20):\n",
        "    x1,x2,s1,s2=trainer(x1,x2,s1,s2)\n",
        "    results.append((x1,x2))\n",
        "  print('epoch %d,x1 %f,x2 %f'%(i+1,x1,x2))\n",
        "  return results\n",
        "\n",
        "show_trace_2d(f_2d,train_2d(gd_2d))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 20,x1 -0.943467,x2 -0.000073\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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Cjc5+7dxcWxCEMxIRikox2GlM+paswbIUl65r5tJ1zdxx47n86kAvP9h9nB/t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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2fx87mRf_ap",
        "colab_type": "text"
      },
      "source": [
        "可以看到，同一位置上，目标函数在竖直方向（ $x_2$ 轴方向）比在水平方向（ $x_1$ 轴方向）的斜率的绝对值更大。因此，给定学习率，梯度下降迭代自变量时会使自变量在竖直方向比在水平方向移动幅度更大。那么，我们需要一个较小的学习率从而避免自变量在竖直方向上越过目标函数最优解。然而，这会造成自变量在水平方向上朝最优解移动变慢。\n",
        "\n",
        "下面我们试着将学习率调得稍大一点，此时自变量在竖直方向不断越过最优解并逐渐发散。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nU7PqcToCKBc",
        "colab_type": "code",
        "outputId": "c3bba28f-17cb-4431-d125-13648f61c180",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "eta=0.6\n",
        "show_trace_2d(f_2d,train_2d(gd_2d))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 20,x1 -0.387814,x2 -1673.365109\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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U74/u2YhIU1JmkkhdllNx99uB28s8dxVwVYn2pcDSEu1rCGZ7iYgMHxXgE2m0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\nERGp2f8H64Ezd+kPI0wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xFHdzmvLgIUg",
        "colab_type": "text"
      },
      "source": [
        "###7.4.2. 动量法\n",
        "动量法的提出是为了解决梯度下降的上述问题。由于小批量随机梯度下降比梯度下降更为广义，本章后续讨论将沿用“小批量随机梯度下降”一节中时间步 $t $的小批量随机梯度 $\\boldsymbol{g}_t$的定义。设时间步 $t$ 的自变量为 $x_t$ ，学习率为$\\eta_t$。 在时间步 0 ，动量法创建速度变量 $v_0$ ，并将其元素初始化成0。在时间步 $t>0$ ，动量法对每次迭代的步骤做如下修改：\n",
        "$$\n",
        "\\begin{split}\\begin{aligned}\n",
        "\\boldsymbol{v}_t &\\leftarrow \\gamma \\boldsymbol{v}_{t-1} + \\eta_t \\boldsymbol{g}_t, \\\\\n",
        "\\boldsymbol{x}_t &\\leftarrow \\boldsymbol{x}_{t-1} - \\boldsymbol{v}_t,\n",
        "\\end{aligned}\\end{split}\n",
        "$$\n",
        "其中，动量超参数 $\\gamma$满足$0 \\leq \\gamma < 1$。当 $\\gamma=0$时，动量法等价于小批量随机梯度下降。\n",
        "\n",
        "在解释动量法的数学原理前，让我们先从实验中观察梯度下降在使用动量法后的迭代轨迹。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LLPboyuPCzok",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def momentum_2d(x1,x2,v1,v2):\n",
        "  v1=gamma*v1+eta*0.2*x1\n",
        "  v2=gamma*v2+eta*4*x2\n",
        "  return x1-v1,x2-v2,v1,v2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QnhJ_KiwDZ9M",
        "colab_type": "code",
        "outputId": "8ea35ac9-4397-42b1-ee7e-3555b04eea34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "eta,gamma=0.4,0.5\n",
        "show_trace_2d(f_2d,train_2d(momentum_2d))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 20,x1 -0.062843,x2 0.001202\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3wst3wGn/efCfE+2DdT+D538CsSAcfqFKZKtaNHZt1Wg0wDgKhZTyjKGuCSH2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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NGwAplsagycR",
        "colab_type": "text"
      },
      "source": [
        "可以看到使用较小的学习率 $\\eta=0.4$ 和动量超参数 $\\gamma=0.5$ 时，动量法在竖直方向上的移动更加平滑，且在水平方向上更快逼近最优解。下面使用较大的学习率 $\\eta=0.6$ ，此时自变量也不再发散。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3Lf9-eXD1aF",
        "colab_type": "code",
        "outputId": "799055c7-4710-4e7e-ac27-0327a1189877",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "eta=0.6\n",
        "show_trace_2d(f_2d,train_2d(momentum_2d))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 20,x1 0.007188,x2 0.002553\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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379nNLdsOoJTir85YxkfOXs38mlBuOqM1fGWl8Y+/4Bu5eU+3ue1vTQLRP+8t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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EmqEzUOdhEDP",
        "colab_type": "text"
      },
      "source": [
        "####7.4.2.1. 指数加权移动平均\n",
        "为了从数学上理解动量法，让我们先解释一下指数加权移动平均（exponentially weighted moving average）。给定超参数$0 \\leq \\gamma < 1$，当前时间步 $t$ 的变量 $y_t$ 是上一时间步 $t-1$ 的变量 $y_{t-1} $和当前时间步另一变量 $x_t $的线性组合：\n",
        "$$\n",
        "y_t = \\gamma y_{t-1} + (1-\\gamma) x_t.\n",
        "$$\n",
        "我们可以对 $y_t$ 展开：\n",
        "$$\n",
        "\\begin{split}\\begin{aligned}\n",
        "y_t  &= (1-\\gamma) x_t + \\gamma y_{t-1}\\\\\n",
        "         &= (1-\\gamma)x_t + (1-\\gamma) \\cdot \\gamma x_{t-1} + \\gamma^2y_{t-2}\\\\\n",
        "         &= (1-\\gamma)x_t + (1-\\gamma) \\cdot \\gamma x_{t-1} + (1-\\gamma) \\cdot \\gamma^2x_{t-2} + \\gamma^3y_{t-3}\\\\\n",
        "         &\\ldots\n",
        "\\end{aligned}\\end{split}\n",
        "$$\n",
        "令$n = 1/(1-\\gamma)$，那么$\\left(1-1/n\\right)^n = \\gamma^{1/(1-\\gamma)}$。因为$$\\lim_{n \\rightarrow \\infty}  \\left(1-\\frac{1}{n}\\right)^n = \\exp(-1) \\approx 0.3679,$$\n",
        "\n",
        "所以当$\\gamma \\rightarrow 1$时，$\\gamma^{1/(1-\\gamma)}=\\exp(-1)$，如$0.95^{20} \\approx \\exp(-1)$。如果把 exp(−1) 当作一个比较小的数，我们可以在近似中忽略所有含$\\gamma^{1/(1-\\gamma)}$和比$\\gamma^{1/(1-\\gamma)}$更高阶的系数的项。例如，当 $\\gamma=0.95$时，\n",
        "$$\n",
        "y_t \\approx 0.05 \\sum_{i=0}^{19} 0.95^i x_{t-i}.\n",
        "$$\n",
        "因此，在实际中，我们常常将 $y_t $看作是对最近 $1/(1-\\gamma)$个时间步的 $x_t$ 值的加权平均。例如，当$\\gamma = 0.95$时， $y_t$ 可以被看作对最近20个时间步的 $x_t$ 值的加权平均；当$\\gamma = 0.9$时， $y_t$ 可以看作是对最近10个时间步的 $x_t$ 值的加权平均。而且，离当前时间步 $t$ 越近的 $x_t$ 值获得的权重越大（越接近1）。\n",
        "\n",
        "####7.4.2.2. 由指数加权移动平均理解动量法\n",
        "现在，我们对动量法的速度变量做变形：\n",
        "$$\n",
        "\\boldsymbol{v}_t \\leftarrow \\gamma \\boldsymbol{v}_{t-1} + (1 - \\gamma) \\left(\\frac{\\eta_t}{1 - \\gamma} \\boldsymbol{g}_t\\right).\n",
        "$$\n",
        "由指数加权移动平均的形式可得，速度变量 $v_t$ 实际上对序列$\\{\\eta_{t-i}\\boldsymbol{g}_{t-i} /(1-\\gamma):i=0,\\ldots,1/(1-\\gamma)-1\\}$做了指数加权移动平均。换句话说，相比于小批量随机梯度下降，动量法在每个时间步的自变量更新量近似于将前者对应的最近 $1/(1-\\gamma)$个时间步的更新量做了指数加权移动平均后再除以 $1-\\gamma$\n",
        "。所以，在动量法中，自变量在各个方向上的移动幅度不仅取决当前梯度，还取决于过去的各个梯度在各个方向上是否一致。在本节之前示例的优化问题中，所有梯度在水平方向上为正（向右），而在竖直方向上时正（向上）时负（向下）。这样，我们就可以使用较大的学习率，从而使自变量向最优解更快移动。\n",
        "\n",
        "###7.4.3. 从零开始实现\n",
        "相对于小批量随机梯度下降，动量法需要对每一个自变量维护一个同它一样形状的速度变量，且超参数里多了动量超参数。实现中，我们将速度变量用更广义的状态变量states表示。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-InNFn3fD8su",
        "colab_type": "code",
        "outputId": "0e87eb0d-1154-40c4-aae5-0b301773dbcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "def get_data_ch7():\n",
        "  data=np.genfromtxt('/content/drive/My Drive/data/d2l-zh-tensoflow/airfoil_self_noise.dat',delimiter='\\t')\n",
        "  data=(data-data.mean(axis=0))/data.std(axis=0)\n",
        "  return tf.constant(data[:1500,:-1]),tf.constant(data[:1500,-1])\n",
        "\n",
        "features,labels=get_data_ch7()  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_tHnfiuEizM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def init_momentum_states():\n",
        "  v_w=tf.Variable(tf.zeros(shape=(features.shape[1],1),dtype=tf.double))\n",
        "  v_b=tf.Variable(tf.zeros(1,dtype=tf.double))\n",
        "  return (v_w,v_b)\n",
        "\n",
        "def sgd_momentum(params,states,hyperparams,loss,t):\n",
        "  for p,v in zip(params,states):\n",
        "    dl_dp=t.gradient(loss,p)\n",
        "    v.assign(hyperparams['momentum']*v+hyperparams['lr']*dl_dp)\n",
        "    p.assign_sub(v)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKi-HrUdHZzc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_ch7(trainer_fn,states,hyperparams,features,labels,batch_size=10,num_epochs=2):\n",
        "  net,loss=linreg,squared_loss\n",
        "  w=tf.Variable(tf.random.normal(stddev=0.01,shape=(features.shape[1],1),dtype=tf.double))\n",
        "  b=tf.Variable(tf.zeros(1,dtype=tf.double))\n",
        "\n",
        "  def eval_loss():\n",
        "    return tf.reduce_mean(loss(net(features,w,b),labels)).numpy()\n",
        "\n",
        "  ls=[eval_loss()]\n",
        "\n",
        "  data_iter=Dataset.from_tensor_slices((features,labels)).shuffle(buffer_size=1000).batch(batch_size)\n",
        "\n",
        "  for _ in range(num_epochs):\n",
        "    start=time.time()\n",
        "    for batch_i,(X,y) in enumerate(data_iter):\n",
        "      with tf.GradientTape(persistent=True) as t:\n",
        "        t.watch([w,b])\n",
        "        l=tf.reduce_mean(loss(net(X,w,b),y)) #使用平均损失\n",
        "\n",
        "      trainer_fn([w,b],states,hyperparams,l,t) #迭代模型参数\n",
        "\n",
        "      if(batch_i+1)*batch_size %100==0:\n",
        "        ls.append(eval_loss()) #每100个样本记录下当前训练误差\n",
        "  # 打印结果和作图\n",
        "  print('loss: %f, %f sec per epoch' % (ls[-1], time.time() - start))\n",
        "  set_figsize()\n",
        "  plt.plot(np.linspace(0, num_epochs, len(ls)), ls)\n",
        "  plt.xlabel('epoch')\n",
        "  plt.ylabel('loss')  \n",
        "\n",
        "def use_svg_display():\n",
        "  #用矢量图显示\n",
        "  display.set_matplotlib_formats('svg')\n",
        "def set_figsize(figsize=(3.5,2.5)):\n",
        "  use_svg_display()\n",
        "  #设置图的尺寸\n",
        "  plt.rcParams['figure.figsize']=figsize\n",
        "  \n",
        "  \n",
        "def linreg(X,w,b):\n",
        "  return tf.matmul(X,w)+b \n",
        "  \n",
        "def squared_loss(y_hat,y):\n",
        "  return (y_hat-tf.reshape(y,shape=y_hat.shape))**2/2    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NeHKy-Rkja4Z",
        "colab_type": "text"
      },
      "source": [
        "我们先将动量超参数momentum设0.5，这时可以看成是特殊的小批量随机梯度下降：其小批量随机梯度为最近2个时间步的2倍小批量梯度的加权平均。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFSwVbEEHniE",
        "colab_type": "code",
        "outputId": "49b39272-0157-41d7-956a-7e802df509a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "train_ch7(sgd_momentum,init_momentum_states(),{'lr':0.02,'momentum':0.5},features,labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loss: 0.248673, 0.431266 sec per epoch\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vtOrPRkLjeaQ",
        "colab_type": "text"
      },
      "source": [
        "将动量超参数momentum增大到0.9，这时依然可以看成是特殊的小批量随机梯度下降：其小批量随机梯度为最近10个时间步的10倍小批量梯度的加权平均。我们先保持学习率0.02不变。"
      ]
    },
    {
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        "id": "PfV-fLFTIFKF",
        "colab_type": "code",
        "outputId": "2fa1e52c-c8d9-4aca-a2b9-ebe373ca0b06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
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      },
      "source": [
        "train_ch7(sgd_momentum,init_momentum_states(),{'lr':0.02,'momentum':0.9},features,labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loss: 0.272450, 0.440948 sec per epoch\n"
          ],
          "name": "stdout"
        },
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            "tags": []
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjpGYWJdjqYW",
        "colab_type": "text"
      },
      "source": [
        "可见目标函数值在后期迭代过程中的变化不够平滑。直觉上，10倍小批量梯度比2倍小批量梯度大了5倍，我们可以试着将学习率减小到原来的1/5。此时目标函数值在下降了一段时间后变化更加平滑。"
      ]
    },
    {
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      "source": [
        "train_ch7(sgd_momentum,init_momentum_states(),{'lr':0.004,'momentum':0.9},features,labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loss: 0.242490, 0.432922 sec per epoch\n"
          ],
          "name": "stdout"
        },
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MdeedHqRjvGC",
        "colab_type": "text"
      },
      "source": [
        "###7.4.4. 简洁实现"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A01mQHeiIrDt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_keras_ch7(trainer_name,features,labels,batch_size=10,num_epochs=2):\n",
        "  net=keras.Sequential(keras.layers.Dense(1))\n",
        "  loss=losses.mean_squared_error\n",
        "  data_iter=Dataset.from_tensor_slices((features,labels)).shuffle(buffer_size=1000).batch(batch_size)\n",
        "  net.compile(optimizer=trainer_name,loss=loss)\n",
        "  history=net.fit_generator(data_iter,epochs=num_epochs)\n",
        "  loss_ls=history.history['loss']\n",
        "  set_figsize()\n",
        "  plt.plot(np.linspace(0,num_epochs,len(loss_ls)),loss_ls)\n",
        "  plt.xlabel('epoch')\n",
        "  plt.ylabel('loss')\n",
        "  plt.show()\n",
        "  return history"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ClEK_sPDI6cd",
        "colab_type": "code",
        "outputId": "a475188b-2b1a-4902-d34c-30f958c714bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        }
      },
      "source": [
        "train_keras_ch7(keras.optimizers.SGD(learning_rate=0.004,momentum=0.9),features,labels,batch_size=10,num_epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "150/150 [==============================] - 1s 5ms/step - loss: 0.5574\n",
            "Epoch 2/10\n",
            "150/150 [==============================] - 1s 5ms/step - loss: 0.4977\n",
            "Epoch 3/10\n",
            "150/150 [==============================] - 1s 5ms/step - loss: 0.4981\n",
            "Epoch 4/10\n",
            "150/150 [==============================] - 1s 5ms/step - loss: 0.4981\n",
            "Epoch 5/10\n",
            "150/150 [==============================] - 1s 5ms/step - loss: 0.4981\n",
            "Epoch 6/10\n",
            "150/150 [==============================] - 1s 5ms/step - loss: 0.4981\n",
            "Epoch 7/10\n",
            "150/150 [==============================] - 1s 5ms/step - loss: 0.4981\n",
            "Epoch 8/10\n",
            "150/150 [==============================] - 1s 5ms/step - loss: 0.4981\n",
            "Epoch 9/10\n",
            "150/150 [==============================] - 1s 5ms/step - loss: 0.4981\n",
            "Epoch 10/10\n",
            "150/150 [==============================] - 1s 5ms/step - loss: 0.4981\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f4c07422048>"
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        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}